Sensor networks are widely used for monitoring and surveillance. Multiple sensors are positioned so as to collect raw environmental data which is then processed for monitoring and decision-making Sensor networks endeavor to provide accurate and timely detection of signals and events that occur in an external environment. The signals may be transient, periodic or a combination thereof. The transitory nature of the signals exacerbates problems with detecting the signals. For example, collecting samples from distributed sensors to detect the presence of a signal in additive white Gaussian noise (AWGN) can be unreliable because of the presence of noise and also because some of the samples may be lost. In general, for any information gathering and detection system, missing and noisy samples lead to performance degradation because the information contained in these samples is either degraded or lost.
In cases where sensors are deployed to detect certain transient events, or other sudden changes in the environment, and report these events to a fusion center, the sensors report the samples to the fusion center through erasure channels where some of the samples are lost. Missing samples can be caused by fading, interference, network congestion, and other factors. Because of the noisy nature of the measurements it is not possible to determine the value of the missing samples and hence such samples cannot be recovered.
A key to preventing performance degradation in such a distributed decision making system is to recover the lost information, such as the lost signal energy, in missing samples. One solution to this problem is to compensate for the possibility of missing samples by operating the system under fixed oversampling which results in an increased sample size. This solution, however, is inefficient and wasteful in terms of system resources and tends to overburden the network, causing congestion.
Error correction coding is commonly used in wireless communications to reduce the effect of noise on samples by utilizing coding to recover the original samples from the received noisy versions. However, this method does not completely eliminate the occurrence of missing samples and it introduces additional complexity at the sensor level. Moreover, both of the above methods fail to take into consideration the difference between samples. For example, in the detection of transient signals, the detection performance depends not only on how many samples are missing, but also on which samples are missing.
Remote decision making is an important aspect within many monitoring/information gathering systems, such as sensor networks. Any uncertainty/losses in the collected data deteriorate the Quality of Information (QoI) that can be derived from the collected data. A major concern in such systems is loss of data and/or its quality that occur between the information gathering end-point and the fusion center, e.g., due to imperfections of the communication links and the communication nodes along the path between the two end-points.
In general, since data processing/aggregation must be done in a timely manner, loss of data affects the QoI presented to the application layer by the fusion center. As a result the derived QoI may be lower than the levels prescribed by the higher applications. Known methods address this problem in a separated approach. The system is first partitioned into layers, consisting of information collection, reporting, and processing, and then modularized solutions are developed to improve the function of each layer.
Such a separated approach sacrifices performance for simplicity, and for complex systems, it often fails to provide any ultimate QoI guarantee for the supported applications. Another drawback of the layered approach is that it ignores the active interactions between layers, especially the possibility that the processing module can provide feedback to information collection and reporting modules to improve the overall QoI.
Therefore there is a need for an information gathering system to overcome the above-described shortcomings.